On-chip cascaded MZI reconfigurable quantum network based on lithium niobate

Abstract

The invention discloses on-chip cascaded MZI reconfigurable quantum network architecture based on lithium niobate. The on-chip cascaded MZI reconfigurable quantum network architecture comprises an input waveguide array serving as a neural network input layer, a plurality of MZIs which are optically connected with the input waveguide array and serve as a neural network hidden layer, a saturable absorber array, a nonlinear optical unit and a detector array serving as a neural network output layer. The MZIs are connected with each other and linearly converted into second array optical signals according to the first array optical signals; each saturable absorber in the saturable absorber array receives a corresponding optical signal in the second array optical signal and nonlinearly converts the optical signal into a third array optical signal, and the third array optical signal is detected by the detector array. According to the invention, the technical problem of on-chip coherent opticalneuromorphic calculation based on a photon integrated circuit is solved, and the limitation of calculation efficiency and power consumption in micro-electronic and hybrid optical electronic implementation is eliminated by a universal and reconfigurable quantum optical neural network.

Description

technical field [0001] The invention relates to a technology in the field of quantum computing, in particular to an on-chip cascaded Mach-Zehnder modulator (MZI) reconfigurable quantum network based on lithium niobate. Background technique [0002] Optical Neural Networks (ONNW) is based on photonic integrated circuits to achieve neuromorphic computing, usually including an input layer, at least one hidden layer and an output layer. In each layer, information is propagated through the neural network by a linear combination (such as matrix multiplication) and a nonlinear activation function applied to the result of the linear combination. When training an artificial neural network model, data is fed into the input layer and the output is computed through a forward propagation step. The parameters are then optimized through a backpropagation process. [0003] Linear transformations (and some nonlinear transformations) can be performed at the speed of light in optical neural ...

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Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention proposes an on-chip cascaded MZI reconfigurable quantum network architecture based on lithium niobate to solve the existing technical problems of on-chip coherent optical neuromorphic computing based on photonic integrated circuits. Reconfigurable Quantum Optical Neural Networks (QONN) Overcome Computational Efficiency and Power Consumption Limitations in Microelectronics and Hybrid Optic-Electronics Implementations

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